Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "180" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460013 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.117559 | 15.595807 | -1.175557 | 11.754991 | 1.130890 | 6.936142 | 25.065860 | 3.516805 | 0.5880 | 0.0511 | 0.4947 | nan | nan |
| 2460012 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.083577 | 14.654790 | -1.338424 | 11.532989 | 1.224702 | 7.641624 | 26.631121 | 3.940350 | 0.5804 | 0.0548 | 0.4773 | nan | nan |
| 2460011 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.033605 | 15.716819 | -1.974218 | 15.441394 | 1.728016 | 15.764820 | 22.719977 | 3.232038 | 0.6005 | 0.0586 | 0.4825 | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.113065 | 17.158305 | -1.274062 | 12.787128 | 0.508178 | 10.377942 | 13.983847 | 3.120817 | 0.6123 | 0.0596 | 0.4869 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.187387 | 15.911076 | -1.560227 | 14.069163 | 0.621559 | 8.747935 | 16.834014 | 3.013876 | 0.6147 | 0.0600 | 0.4886 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 285.721738 | 285.652846 | inf | inf | 2887.518079 | 2792.838826 | 3650.127055 | 3368.798730 | nan | nan | nan | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.031573 | 14.624835 | -1.246466 | 12.124655 | 0.286862 | 7.146688 | 19.185366 | 3.346261 | 0.6219 | 0.0587 | 0.4895 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 98.66% | 98.66% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3380 | 0.3386 | 0.2225 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.014916 | 12.287697 | -1.145027 | 10.260256 | 1.435260 | 10.089348 | 24.851731 | 2.480153 | 0.6116 | 0.0497 | 0.5100 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.152660 | 13.412354 | -1.210681 | 11.019409 | 0.459453 | 9.485076 | 36.201862 | 4.042281 | 0.6217 | 0.0561 | 0.5149 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.178284 | 14.510408 | -1.184486 | 13.482025 | 0.084377 | 9.167174 | 15.826168 | 1.634341 | 0.6348 | 0.0543 | 0.5265 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.249494 | 14.662515 | -1.565246 | 12.687186 | 0.837305 | 9.348437 | 14.353904 | 1.574854 | 0.6189 | 0.0601 | 0.5031 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.336897 | 14.214648 | -1.314553 | 11.119530 | 1.011314 | 9.435589 | 12.503814 | 1.879693 | 0.6132 | 0.0524 | 0.5027 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 98.74% | 0.00% | - | - | -0.065024 | 13.300796 | -1.351134 | 10.319431 | 0.486179 | 10.826663 | 14.317703 | 2.982657 | 0.5862 | 0.0461 | 0.4846 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.326976 | 16.544895 | -1.362726 | 10.919060 | 0.052455 | 10.645380 | 13.822472 | 1.295786 | 0.6284 | 0.0508 | 0.5305 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.362664 | 13.622910 | -1.349346 | 10.611488 | 0.252298 | 10.958609 | 13.677314 | 1.570866 | 0.6256 | 0.0536 | 0.5207 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.410315 | 13.810861 | -1.104156 | 9.698285 | 0.243907 | 9.160904 | 12.340955 | 1.039390 | 0.6184 | 0.0498 | 0.5157 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.302552 | 16.170805 | -1.443096 | 10.906332 | 0.180610 | 13.118299 | 14.883937 | 1.002743 | 0.6204 | 0.0493 | 0.5166 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 99.30% | 0.00% | - | - | -0.238933 | 13.588147 | -1.391195 | 10.749132 | 0.899097 | 7.905734 | 15.280766 | 2.844933 | 0.6293 | 0.0540 | 0.5242 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.091176 | 16.594780 | -1.485995 | 11.604706 | 1.283931 | 11.145698 | 9.307592 | 9.865932 | 0.6424 | 0.0523 | 0.5086 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.219152 | 15.039710 | -1.429882 | 10.804673 | 0.449839 | 8.518054 | 19.413062 | 2.743716 | 0.6277 | 0.0519 | 0.5227 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 98.27% | 0.00% | - | - | 0.505360 | 14.504414 | -0.973804 | 11.184698 | 1.800512 | 12.089798 | 2.308120 | 4.299921 | 0.6412 | 0.0593 | 0.5207 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.300635 | 14.134948 | -1.402808 | 10.610752 | 0.091164 | 11.061104 | 10.359978 | 6.922607 | 0.6537 | 0.0542 | 0.5136 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.258026 | 11.697742 | -0.986014 | 9.040923 | 1.122173 | 5.210447 | 0.390141 | 3.304802 | 0.6920 | 0.0536 | 0.5301 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.408270 | 13.057447 | -1.530493 | 11.300564 | 0.923935 | 12.216082 | 12.798934 | 1.621366 | 0.6284 | 0.0535 | 0.5211 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.244231 | 12.539043 | -1.563824 | 10.317538 | 1.146216 | 10.682390 | 0.844778 | 5.367506 | 0.6629 | 0.0552 | 0.5222 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.420400 | 13.207076 | -1.519445 | 9.665449 | 0.484007 | 10.006501 | 13.173167 | 1.200686 | 0.6207 | 0.0500 | 0.5199 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.435586 | 13.346462 | -1.541414 | 10.403396 | 0.635375 | 10.874296 | 19.675656 | 2.108509 | 0.6205 | 0.0476 | 0.5225 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.332364 | 14.118339 | -1.500474 | 10.239643 | 0.491793 | 11.214190 | 15.311675 | 2.113203 | 0.5852 | 0.0554 | 0.4818 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.432570 | 13.555617 | -1.608398 | 10.675678 | 0.874553 | 10.739358 | 13.873481 | 2.020690 | 0.6258 | 0.0496 | 0.5217 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 25.065860 | -0.117559 | 15.595807 | -1.175557 | 11.754991 | 1.130890 | 6.936142 | 25.065860 | 3.516805 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 26.631121 | -0.083577 | 14.654790 | -1.338424 | 11.532989 | 1.224702 | 7.641624 | 26.631121 | 3.940350 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 22.719977 | -0.033605 | 15.716819 | -1.974218 | 15.441394 | 1.728016 | 15.764820 | 22.719977 | 3.232038 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 17.158305 | 0.113065 | 17.158305 | -1.274062 | 12.787128 | 0.508178 | 10.377942 | 13.983847 | 3.120817 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 16.834014 | -0.187387 | 15.911076 | -1.560227 | 14.069163 | 0.621559 | 8.747935 | 16.834014 | 3.013876 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Power | inf | 285.652846 | 285.721738 | inf | inf | 2792.838826 | 2887.518079 | 3368.798730 | 3650.127055 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 19.185366 | -0.031573 | 14.624835 | -1.246466 | 12.124655 | 0.286862 | 7.146688 | 19.185366 | 3.346261 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 24.851731 | -0.014916 | 12.287697 | -1.145027 | 10.260256 | 1.435260 | 10.089348 | 24.851731 | 2.480153 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 36.201862 | -0.152660 | 13.412354 | -1.210681 | 11.019409 | 0.459453 | 9.485076 | 36.201862 | 4.042281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 15.826168 | 0.178284 | 14.510408 | -1.184486 | 13.482025 | 0.084377 | 9.167174 | 15.826168 | 1.634341 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 14.662515 | -0.249494 | 14.662515 | -1.565246 | 12.687186 | 0.837305 | 9.348437 | 14.353904 | 1.574854 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 14.214648 | -0.336897 | 14.214648 | -1.314553 | 11.119530 | 1.011314 | 9.435589 | 12.503814 | 1.879693 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 14.317703 | -0.065024 | 13.300796 | -1.351134 | 10.319431 | 0.486179 | 10.826663 | 14.317703 | 2.982657 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 16.544895 | -0.326976 | 16.544895 | -1.362726 | 10.919060 | 0.052455 | 10.645380 | 13.822472 | 1.295786 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 13.677314 | 13.622910 | -0.362664 | 10.611488 | -1.349346 | 10.958609 | 0.252298 | 1.570866 | 13.677314 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 13.810861 | 13.810861 | -0.410315 | 9.698285 | -1.104156 | 9.160904 | 0.243907 | 1.039390 | 12.340955 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 16.170805 | 16.170805 | -0.302552 | 10.906332 | -1.443096 | 13.118299 | 0.180610 | 1.002743 | 14.883937 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 15.280766 | -0.238933 | 13.588147 | -1.391195 | 10.749132 | 0.899097 | 7.905734 | 15.280766 | 2.844933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 16.594780 | 16.594780 | -0.091176 | 11.604706 | -1.485995 | 11.145698 | 1.283931 | 9.865932 | 9.307592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 19.413062 | 15.039710 | -0.219152 | 10.804673 | -1.429882 | 8.518054 | 0.449839 | 2.743716 | 19.413062 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 14.504414 | 0.505360 | 14.504414 | -0.973804 | 11.184698 | 1.800512 | 12.089798 | 2.308120 | 4.299921 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 14.134948 | -0.300635 | 14.134948 | -1.402808 | 10.610752 | 0.091164 | 11.061104 | 10.359978 | 6.922607 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 11.697742 | -0.258026 | 11.697742 | -0.986014 | 9.040923 | 1.122173 | 5.210447 | 0.390141 | 3.304802 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 13.057447 | 13.057447 | -0.408270 | 11.300564 | -1.530493 | 12.216082 | 0.923935 | 1.621366 | 12.798934 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 12.539043 | 12.539043 | -0.244231 | 10.317538 | -1.563824 | 10.682390 | 1.146216 | 5.367506 | 0.844778 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | nn Shape | 13.207076 | -0.420400 | 13.207076 | -1.519445 | 9.665449 | 0.484007 | 10.006501 | 13.173167 | 1.200686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 19.675656 | 13.346462 | -0.435586 | 10.403396 | -1.541414 | 10.874296 | 0.635375 | 2.108509 | 19.675656 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 15.311675 | -0.332364 | 14.118339 | -1.500474 | 10.239643 | 0.491793 | 11.214190 | 15.311675 | 2.113203 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 180 | N13 | RF_maintenance | ee Temporal Discontinuties | 13.873481 | 13.555617 | -0.432570 | 10.675678 | -1.608398 | 10.739358 | 0.874553 | 2.020690 | 13.873481 |